We prefer longer videos with sparse key-steps to generate practical solutions.
We select CMU-MMAC [1]}, EGTEA Gaze+ [2]}, MECCANO [3]}, and EPIC-Tents [4]} based on the above criteria.
CMU-MMAC contains recordings of subjects performing the same task from one egocentric and four third-person views.
Therefore, by using it, we compare the performance of CnC between egocentric and third-person views.
Though these four datasets include a diverse range of tasks, they do not contain tasks where the subject works in a constrained environment and deals with small objects (e.g., screws).
To alleviate this, we include manually recorded videos of assembling and disassembling a Personal Computer (PC).
This addition makes the dataset diverse and challenging in terms of variability in the size of objects involved and the complexity of key-steps (e.g., fixing the motherboard requires fastening nine screws).
EgoProceL contains videos and key-step annotations for multiple tasks from CMU-MMAC [1]} and EGTEA Gaze+ [2]} and individual tasks like toy-bike assembly [3]}, tent assembly [4]}, PC assembly, and PC disassembly.
EgoProceL consists of 62 hours of annotated egocentric videos, including 16 tasks with an average duration of 13 minutes.
To annotate the videos for key-steps, we create a list of key-steps for each task, e.g., assembling a PC requires `Fix motherboard', `Fix hard disk', ..., `Place the cabinet cover'.
We use ELAN [9]} to annotate each video by marking the start and end location during which the key-step occurs.
Along with various procedure learning tasks, EgoProceL is appropriate for understanding hand-object interaction, action forecasting and recognition, and a shared study of videos and text.
fig:annotations shows some example annotations and tab:datasetstats compares EgoProceL with existing datasets.
<FIGURE>We also considered a few other datasets that did not satisfy the requirements mentioned above [10]}, [11]}. The reasons for their non-inclusion are given in the supplementary.
Correspond and Cut Framework for Procedure Learning
Humans often follow the same steps to perform any particular task, though the order of steps might be different.
This work proposes a methodology which, given a set of videos of humans performing a task, learns similar embeddings across videos for the key-steps required to complete a task.
Once we have the embeddings, learning a procedure reduces to clustering the embeddings for localizing the key-steps among all the videos.
To learn the embeddings, we exploit temporal correspondences between the videos of the same task.
For that purpose, we train a representation learning network using the proposed TC3I loss.
TC3I builds on top of existing temporal video alignment methods [12]}, [13]}.
After learning the embeddings, we use PCM, shown in fig:methodology, to cluster and localize the underlying key-steps.
PCM models the clustering problem as a multi-label graph cut problem and solves it to localize the key-steps.
Once we localize the key-steps using PCM, we use the frame's relative location in a video to generate the key-step ordering for each video.
<FIGURE>Notation:
CnC takes in \(V=\lbrace V_i: i \in \mathbb {N}, 1 \le i \le n\rbrace \)  untrimmed videos of the same task, where \(n\)  is the total number of videos.
Each of the \(n\)  videos can have a different number of frames.
We denote the embedding function used to generate the frame-level embeddings as \(f_{\theta }\) , which is a neural network with parameters \(\theta \) .
A video \(V_k\)  with \(m\)  frames is denoted as \(V_k = \lbrace f_k^1, f_k^2, \dots , f_k^m\rbrace \)  and the video's frame-level embeddings are denoted as \(f_{\theta }(V_k) = \lbrace v_k^1, v_k^2, \dots , v_k^m\rbrace \) .
We assume \(K\)  key-steps in a task, where \(K\)  is a hyper-parameter.
Learning the Embeddings using the TC3I loss
We aim to learn similar embeddings for the frames with comparable semantic information across different temporal locations from multiple videos.
For that purpose, we use Temporal Cycle Consistency (TCC) [12]} to find correspondences across time in videos.
Consider two videos \(V_1\)  and \(V_2\) , with lengths \(p\)  and \(q\) , respectively.
To check if a point \(v_1^i\)  in \(V_1\)  is cycle consistent, its nearest neighbour \(v_2^j = \arg \min _{v_2 \in V_2} \Vert v_1^i - v_2 \Vert \)  is calculated in \(V_2\) . Then the process is repeated for \(v_2^j\)  in \(V_1\)  to get \(v_1^k = \arg \min _{v_1 \in V_1} \Vert v_2^j - v_1 \Vert \) .
If \(i=k\) , then the point is considered cycle consistent.
An acceptable embedding space consists of a maximum number of cycle-consistent points for a pair of sequences.
Specifically, for a point \(v_1^i\)  in \(V_1\) , we determine its soft nearest neighbor \(\widetilde{v}_2\)  in \(V_2\)  by using the softmax function as follows:
\(\widetilde{v}_2 = \sum _j \alpha _j v_2^j, \quad \text{where} \quad \alpha _j = \frac{e^{-\Vert v_1^i - v_2^j \Vert ^2}}{\sum _k e^{- \Vert v_1^i - v_2^k \Vert ^2}}.\) 
Here \(\alpha _j\)  signifies the similarity between \(v_1^i\)  and individual \(v_2^j \in V_2\) .
Once we have the soft nearest neighbor, a similarity vector \(\beta _1^i\)  is calculated.
\(\beta \)  defines the proximity between \(\widetilde{v}_2\)  and each frame \(v_1^k \in V_1\)  as:
\(\beta _1^i[k] = \frac{e^{-\Vert \widetilde{v}_2 - v_1^k \Vert ^2}}{\sum _j e^{-\Vert \widetilde{v}_2 - v_1^j \Vert ^2}}.\) 
As \(\beta \)  is a discrete distribution of similarities over time, it peaks around the \(i^{th}\)  time index.
To avoid this, a Gaussian prior is applied to \(\beta \)  by minimizing the normalized squared distance \(\frac{|i - \mu |^2}{\sigma ^2}\)  as the objective.
By applying additional variance regularization, \(\beta \)  is enforced to be peaky around \(i\) .
Hence, the final cycle consistency loss between videos \(V_1\)  and \(V_2\) , corresponding to \(i^\text{th}\)  frame of \(V_1\)  is:
\(L(V_1, V_2, v_1^i) = \frac{|i - \mu |^2}{\sigma ^2} + \lambda \log (\sigma ).\) 
Here, \(\mu _i =\sum _k \beta _1^i[k] \times k\)  and \(\sigma ^2_i = \sum _k \beta _1^i[k] \times (k - \mu _i)^2\) , and \(\lambda \)  is the regularization weight.
Formulating TCC in this way ensures the model is not heavily penalized when it cycles back to close-by frames.
We observe that there are many repetitive frames in egocentric videos because of which cycle consistency loss often loops back to similar but temporally far-away frames.
To alleviate the issue, we utilize the Contrastive-Inverse Difference Moment (C-IDM) loss [13]} (a modified form of Inverse Difference Moment [16]}) for applying temporal regularization on each video.
The C-IDM loss between the two frames \(i\)  and \(j\)  of a video \(V_1\)  is computed as:
\(I(V_1,i,j) = \left( 1 - \mathcal {N}(i, j) \right) \gamma (i, j) \max \left( 0, \zeta - d(i, j) \right) + \mathcal {N}(i, j) \frac{d(i, j)}{\gamma (i, j)}.\) 

Here, \(\gamma (i,j) = (i - j)^2 + 1\), \(d(i, j)\)  is the Euclidean distance between \(f_{\theta }(v_1^i)\) , and \(f_{\theta }(v_1^j)\) , \(\zeta \)  is the margin parameter, and \(\mathcal {N}\)  is the neighborhood function such that, \(\mathcal {N}(i, j) = 1\) if \(|i - j| \le \sigma \) , and 0 otherwise. Here, \(\sigma \)  is the window size for separating temporally far away frames.
The C-IDM loss encourages the embeddings of the temporally close frames to be similar and the embeddings of temporally far frames to be dissimilar.
The final loss combines both TCC and C-IDM (referred to as TC3I loss from now on):
\(\text{TC3I}(V_1, V_2) & = \sum _{i \in V_1} L(V_1, V_2, v_1^i)+ \sum _{j \in V_2} L(V_1, V_2, v_2^j) \nonumber \\& + \xi \sum _{i \in V_1} \sum _{j \in V_1} I(V_1,i,j)+ \xi \sum _{i \in V_2} \sum _{j \in V_2} I(V_2,i,j).\) 

Here, \(\xi \)  is a regularization parameter.

Localizing the Key-Steps using the ProCut Module
<FIGURE>Once we learn the embeddings, we aim to localize the key-steps required for performing the task.
Kukleva et al.  [17]} localize the key-steps by generating \(K\)  clusters of embeddings using the K-Means algorithm [18]}. However, they need to assume a fixed order of key-steps to assign frames to the key-steps.
Instead, we propose a novel ProCut Module (PCM) for the purpose. PCM converts the clustering problem to a multi-label graph cut problem [19]}, as described below.
Let \(G = \langle V, E \rangle \)  be a graph consisting of a set of nodes \(V\)  and a set of directed edges \(E\)  connecting them.
The node set \(V\)  consists of \(K\)  terminal nodes representing the key-steps, and non-terminal nodes (equal to the number of frames) representing the embeddings of the frames generated using the Embedder network.
There are two kinds of edges in the graph: t-links connecting non-terminal nodes to the terminal nodes, and n-links connecting two non-terminal nodes.
We use the Fuzzy C-Means algorithm [20]} to assign a cost to the t-links.
The algorithm performs soft clustering and calculates the probability of a frame belonging to each cluster.
We subtract the probability value from 1 to obtain the cost of assigning a frame to each cluster.
The cost value for the n-links is assigned based on the temporal distance between the nodes.
For example, if the nodes are temporally closer (e.g., nodes at positions 1 and 2 in fig:graphcut), the cost of assigning the same label to them is lower, otherwise (e.g., for nodes at positions 1 and 5 in fig:graphcut), the cost is high.
After creating the graph \(G\) , we use \(\alpha \) -Expansion [21]} to find the minimum cost cut.
We use the discovered cut to assign frames to \(K\)  labels.
As shown in fig:graphcut, the lowest costs (highlighted in green) result in assigning the first and second frames to key-step 1, the third and fourth frames to key-step 2, and the last to key-step 3.

Determining Order of the Key-Steps
When it comes to determining the ordering of the key-steps, it makes sense to allow each video to have a distinct key-step ordering as there can be multiple ways to perform a task.
However, current works either use a fixed order of key-steps to decode all the videos [22]}, [17]}, [24]} or do not predict the ordering [25]}, [26]}.
One of the advantages of using CnC to determine the key-step is that it allows each video to have its independent order of the key-steps.
To infer the sequential order of key-steps, we calculate the normalized time for each frame \(v_i^n\)  in video \(V_i\)  consisting of \(p\)  frames as \(T(v_i^n) = \frac{n}{p}\)  [17]}, [24]}. Then we calculate the time instant for each cluster as the average normalized for frames assigned to it.
The clusters are then arranged in increasing order of the average time, providing us with the sequence of key-steps used to perform the task in a video.
Once we have key-step order for all the videos of the same task, we generate their ranked list based on the number of times the subjects followed a particular order.
The order followed the most ends up being at the top of the ranked list.
Doing this enables us to determine different sequential orders of key-steps to accomplish a task.

Implementation Details
We use ResNet-50 [29]} as our backbone network to extract the features.
Motivated by [12]}, for training the Embedder network, we use a pair of training videos at a time, select frames at random within the videos and optimize the proposed TC3I loss until convergence.
The features are extracted from the Conv4c layer and a stack of \(c\)  context frames features is created along the temporal dimension.
We reshape our input video frames to \(224 \times 224\) .
To aggregate the temporal information, we pass the combined features through two 3D convolutional layers followed by a 3D global max pooling layer, two fully-connected layers, and a linear projection layer to output the embeddings of dimension 128.
We set the value of \(K\)  to 7 and compare the performance of CnC with the other values of \(K\)  in tab:selectingk.
Furthermore, for all our experiments, we follow the task-specific settings laid out in [25]}.
We use PyTorch [32]} for all our experiments.

Experiments
Evaluation
Current works compute framewise F1-Score and IoU scores for key-step localization [25]}, [22]}, [17]}, [26]}, [24]}.
The F1-Score is a harmonic mean of precision and recall scores.
For calculating recall, the ratio between the number of frames having correct key-steps prediction and the number of ground truth key-step frames across all the key-steps of a video is calculated.
For precision, the denominator is the number of frames assigned to the key-steps.
For calculations, the one-to-one mapping between the ground truth and prediction is generated using the Hungarian algorithm [38]} following [39]}, [25]}, [22]}, [26]}, [17]}.
However, these metrics tend to assign high scores to models that assign most frames to a single cluster, as the key-step with most frames matches with the background frame's label in the ground truth.
Furthermore, for untrimmed procedure learning videos, most of the frames are background, resulting in high scores.
Shen et al.  [26]} attempt to solve this problem by analyzing the MoF score, but as pointed out in [17]}, MoF is not always suitable for an imbalanced dataset.
Instead, we propose calculating the framewise scores for each key-step separately and then taking the mean of the scores over all the key-steps.
This penalises the cases when there is a large performance difference for different key-step, e.g., when all the frames get assigned to a single key-step.
Upon following this protocol, the scores for all the methods decrease.
This paper presents the results generated using the proposed evaluation protocol unless otherwise mentioned.

Procedure Learning from Third-person Videos
<TABLE>To test the generalizability of CnC on third-person videos and to ensure a fair comparison with existing methods [39]}, [25]}, [48]}, [17]}, [26]}, we perform experiments on third-person procedure learning benchmark datasets: ProceL [22]} and CrossTask [52]}. We obtain the results of previous works from [26]}. Note that here we use the evaluation protocol employed by the previous works [25]}, [22]}, [17]}, [26]}.
As seen in tab:tablethirdpersonresults, CnC outperforms other methods (in terms of the F-Score) utilizing only videos as the input modality.
Further, with only video as the input modality, CnC even manages to perform at par with multi-modal methods.
Previous works have used different forms of self-supervision.
For example, [25]} use the pseudo-labels provided by subset selection and [17]} utilize the relative time-stamps of video frames.
Instead, the comparison in tab:tablethirdpersonresults shows that the signal provided by corresponding frames is superior for the task of procedure learning.

Procedure Learning Results from Egocentric Videos
Baselines:
We consider three baseline methods:

Random. Here we predict the labels by randomly sampling predictions from a uniform distribution with \(K\)  values representing \(K\)  key-steps.

TC3I + HC. Instead of PCM, we use the K-Means algorithm and generate \(K\)  clusters from the representation space.

TC3I + SS. Here, instead of PCM, we use subset selection for the key-step assignment. The algorithm takes in the frame's embeddings and \(M\)  (hyper-parameter) latent states obtained using K-Means [18]}.
It then selects a subset \(S\)  (of size \(K\) ) of the states as key-steps and finds the frames' assignments.
We use the greedy algorithm used in [25]} to perform subset selection. Refer to the supplementary material for the hyper-parameter values.

tab:resultsprocedurelearning summarises the results obtained on EgoProceL using the baselines and proposed CnC.
CnC performs higher than all the three baselines. This is due to (a) the ability of the TC3I loss to learn the representation space where similar key-steps lie close without enforcing any ordering or temporal constraints.
Moreover, TC3I adds temporal coherency to the learned representations by adopting the C-IDM loss [13]} (fig:qualitativeresults).
(b) PCM gains a comprehensive view of the problem by considering the cost of assigning each frame belonging to every key-step and its temporal relationship with the other frames.
<TABLE>CnC performs better on long sequences as the TC3I loss compensates by searching for corresponding frames in the entire length of the videos, making it possible to learn a reasonable representation space despite the length of the videos.
<FIGURE>Further, the results in tab:resultsprocedurelearning show that PCM is superior for key-frame clustering and assignment along with TC3I as it results in the highest F-Score and IoU on EgoProceL.
The gain in performance is because PCM considers the cost of assigning each frame to every key-step and its temporal relationship with the other frames (fig:qualitativeresults).
This allows PCM to gain a comprehensive view of the problem compared to HC, which does not consider the cost of each frame belonging to other key-steps and SS, which has lower generalisation power [25]}.

Egocentric vs. Third-person Videos
<TABLE>Here, we compare the results obtained after training CnC on multiple views from CMU-MMAC [1]}.
As seen in tab:firstpersonvsthirdperson, the frame-wise F1-Score and IoU scores are the highest for the egocentric view.
This is because egocentric videos offer lower occlusion by the expert's body and provide higher visibility of hand-object interactions.
This highlights one of the central hypotheses of this paper: the effectiveness of using egocentric videos over third-person videos for procedure learning.
Also, we observe that the results vary for third-person videos due to the camera placement.
This increases one variable when creating data for procedure learning.
Alternatively, egocentric videos use head-mounted cameras, eliminating uncertainty.

Ablation study
Here, we quantitatively evaluate our design choices.
Due to space constraints, results for [1]} and [2]} are provided here, and the rest are in the supplementary.
<TABLE>Effectiveness of the TC3I Loss:
Here, we replace the TC3I loss in CnC with TCC [12]}, LAV [13]}, and a combination of LAV and TCC [13]} to study the efficacy of the proposed TC3I loss.
TC3I loss in tab:resultsablationtc3i obtains the highest F-Score and IoU.
As observed in our initial set of experiments, TCC loss lacks temporal coherency due to which temporally close frames do not lie close in the learned representation space, resulting in lower results when compared to TC3I and LAV, which account for temporal coherency using the C-IDM loss.
For LAV + TCC, our observations are consistent with [13]} because there is no performance gain when directly combining LAV and TCC losses since LAV works on L2-normalised embeddings, whereas TCC does not [13]}.
The LAV loss performs better than TCC and LAV + TCC; however, the results are not better than TC3I because the Soft-DTW used in LAV accounts for global alignment.
However, LAV does not focus on the per-frame features [13]}, which is beneficial when looking for similar key-steps in different videos.
The TC3I loss overcomes these issues by focusing on correspondences in multiple videos at frame level and adding temporal coherency by adopting the C-IDM loss.

Selecting the value of \(K\)
tab:selectingk contains results of CnC and the baselines as the function of \(K\) .
Additionally, it features the results after replacing PCM with HC and SS as the function of \(K\) .
Here, key observations are: (a) CnC performs the best when \(K=7\) ,
(b) the results do not change significantly for CnC as \(K\)  increases.
However, we observe a decline in the results for HC and SS as K increases, highlighting the effectiveness of PCM for key-step localisation.
<TABLE>
Conclusion
Learning procedures from the visual demonstration of a task by an expert, is an important step in scaling the learning capabilities of autonomous agents. Unlike current state-of-the-art techniques, instead of third-person videos, we have proposed procedure learning from first-person viewpoint. Given the unavailability of the datasets for the purpose, we proposed the EgoProceL containing egocentric videos for procedure learning. We also proposed a new technique, CnC, for procedure learning from egocentric videos that utilize the proposed TC3I loss to learn an embedding space in a self-supervised fashion. Finally, we employ PCM to identify the key-steps. Our results demonstrate the superiority of using the egocentric view and the effectiveness of the proposed technique for procedure learning.
Acknowledgements.
The work was supported in part by the Department of Science and Technology, Government of India, under DST/ICPS/Data-Science project ID T-138. We acknowledge Pravin Nagar and Sagar Verma for sharing the PC Assembly and Disassembly videos recorded at IIIT Delhi. We also acknowledge Jehlum Vitasta Pandit and Astha Bansal for their help with annotating a portion of EgoProceL.

Appendix
Additional details to support the main paper.
<FIGURE>Outline
fig:firstpersonvsthirdperson highlights issues with standard third-person datasets, motivating us to use egocentric videos for procedure learning.
In sec:datasetdetails, we discuss the annotation protocols, task-level details, and datasets excluded while creating the EgoProceL dataset.
In sec:applications, we highlight multiple use-cases for our work.
In subsec:egteaablationresults, we provide additional ablation results on EgoProceL.
To facilitate reproducing the results reported in the main paper and supplementary, subsec:hyper-parameters lists the hyper-parameters used for CnC.
Furthermore, we release the EgoProceL dataset and code for the work on project's webpageLink 1: http://cvit.iiit.ac.in/research/projects/cvit-projects/egoprocel; Mirror link 2: https://sid2697.github.io/.

EgoProceL
This section contains additional details on the proposed EgoProceL dataset.
Annotation Protocols followed for EgoProceL
CMU-MMAC [1]}, EPIC-Tents [4]}, MECCANO [3]}, PC Assembly, PC Disassembly:
A list of key-steps required to perform the task was created upon viewing the videos.
Two annotators were asked to identify the key-steps in the videos and temporally mark the start and end locations.
Once an annotator added temporal segments to the videos, the other annotator verified them.
We use the ELAN software [9]} to annotate the videos.

EGTEA Gaze+ [2]}:
We used the recipes provided by the dataset curators to create the key-step's list for each task.
The dataset offers dense activity annotations for all the videos.
We created a one-to-many mapping between the key-steps and the provided annotations; this accelerated the annotations process.
The mapping generated was used to create key-step annotations for all videos.
Three people further watched the videos and verified the annotations generated.
To accelerate future research, we release the EgoProceL dataset on the project web page REF.

Task-level details of EgoProceL
In tab:datastats, we share the statistics for each of the 16 tasks in the EgoProceL dataset.
Let \(N\)  be the number of videos, \(K\)  be the number of key-steps for a task, \(u_n\)  be the number of unique key-steps and \(g_n\)  be the number of annotated key-steps for \(n^{th}\)  video.
Following [22]}, we calculate the following:
Foreground Ratio
It is the ratio of total duration of the key-steps to the total duration of the video.
This helps to understand the amount of background actions a task has.
The foreground ratio is inversely proportional to the amount of background.
It is calculated as:
\(F = \frac{\sum _{n=1}^{N}\frac{t_k^n}{t_v^n}}{N}\) 
Here, \(t_k^n\)  and \(t_v^n\)  are the key-step duration and video duration for \(n^{th}\)  video, respectively.
The range of \(F\)  is between 0 and 1.
From tab:datastats, we can see that the tasks have significant variance in the foreground ratio.
Conversely, tasks like “PC Assembly” and “Tent Assembly” have a high foreground ratio, suggesting fewer background actions.
On the other hand, tasks like preparing “Bacon and Eggs” and “Turkey Sanwich” have low foreground ratios, suggesting more background actions.
<TABLE>
Missing Key-steps
This measure captures the count of missed key-steps in each video.
It is defined as:
\(M = 1 - \frac{\sum _{n=1}^N u_n}{KN}\) 
The range of \(M\)  is between 0 and 1.
It helps understand if a task can be done even if we miss some steps.
For example, in tab:datastats, “Salad” has the highest missing key-steps ratio suggesting that salad can be made if we miss multiple key-steps.
This makes sense, as one can miss adding mayonnaise to the salad but still create an edible salad.
On the other hand, tasks like “PC Disassembly” and “Pepperoni Pizza” can not afford to miss key-steps as the task won't be complete.
So, for such tasks, we see a missing key-step ratio of 0.

Repeated Key-steps
This measure captures the repetitions of key-steps across the videos.
It is defined as:
\(R = 1 - \frac{\sum _{n=1}^N u_n}{\sum _{n=1}^N g_n}\) 
The range of \(R\)  is between 0 and 1.
Higher values of \(R\)  indicate repetitions of key-steps across videos.
From tab:datastats, we can see preparing “Pasta Salad” has the highest repeated key-steps and preparing “salad” has the lowest.
Methods that do not consider repetitions of the key steps, will not perform well for such tasks.
As CnC takes repetitions of the key steps into consideration, it performs well.

Datasets not included in EgoProceL
As mentioned in the main paper, we followed a set of criteria to select videos from existing datasets for including in EgoProceL. Here we discuss two potential datasets which we could not use for EgoProceL.
The Charades-Ego dataset [10]}, consisting of paired egocentric and third-person videos, is essential for activity recognition. However, it is not practical for procedure learning. The subjects do not perform a series of key-steps to achieve a goal; instead, they perform activities like pouring a drink in a cup and having it. Additionally, the average duration of the videos is \(31.2\)  seconds compared to 13 minutes in EgoProceL, suggesting the briefness of the tasks acted out.
The EPIC-Kitchens dataset [11]}, consisting of 100 hours of kitchen recordings, comes quite close to our requirements. However, due to the unscripted nature of the dataset (which sets it apart from [2]}), it becomes unsuitable. As for procedure learning, we need videos of the same tasks performed multiple times.

Applications
Learning a procedure by observing multiple videos of the same task opens up a range of possible applications.
Monitoring procedures: Consider a system trained to know the key-steps for performing a task; if a new person does the same task again, the system will identify if the person misses a step or does a step differently.
Guidance systems: A system trained to know the key-steps for performing a task can identify the current step and show the next possible step for performing the task.
Automated systems: The proposed framework benefits by enabling automated robotic systems to autonomously learn the key-steps for performing the task by observing the task being performed.
Once the automated system learns the key-steps, the next time, it can do the task without any human assistance.

Additional Experimental Details
Ablation Results
This section contains ablation results on parts of EgoProceL.
tab:resultsablationtc3isup contains the results obtained upon replacing the TC3I loss with TCC [12]}, LAV [13]}, and a combination of LAV and TCC [13]}.
Additionally, tab:selectingksup shows the results obtained upon using various values of \(K\) .
Finally, tab:resultsablationpcmdifflosscmu shows the results obtained after considering different combination of losses along with HC and SS for [1]}, [2]}.
<TABLE>Consistent with the results obtained in the main paper, in tab:resultsablationtc3isup, we observe highest results when using the proposed TC3I loss.
This is because TC3I accounts for the loss of temporal coherency by TCC [12]} with the help of C-IDM loss [13]}.
Additionally, the TC3I loss focuses on correspondences at the frame level as compared to global alignment employed by LAV [13]}.
<TABLE><TABLE>Consistent with our observations in the main paper, in tab:selectingksup, we achieve the highest scores when \(K=7\) .
Additionally, for most cases, CnC results in the highest scores for all the values of \(K\) .
tab:resultsablationpcmdifflosscmu shows the results after using various losses with HC, SS, and PCM for procedure learning [1]}, [2]}.
Nearly all the experiments using PCM achieve the highest scores for other losses.
Additionally, we achieve the highest scores with CnC.
Due to the characteristics of TC3I loss and PCM, the results are consistent with our previous observations.

Hyper-parameters
tab:hyperparameters lists the hyper-parametes used for CnC.
<TABLE>